from __future__ import annotations

from functools import wraps
from typing import TYPE_CHECKING, Any, Callable, Final, Literal, cast, overload

import narwhals as nw
from narwhals import exceptions, functions as nw_f
from narwhals._exceptions import issue_warning
from narwhals._expression_parsing import ExprKind, ExprNode, is_expr
from narwhals._typing_compat import TypeVar, assert_never
from narwhals._utils import (
    Implementation,
    Version,
    deprecate_native_namespace,
    generate_temporary_column_name,
    inherit_doc,
    is_ordered_categorical,
    maybe_align_index,
    maybe_convert_dtypes,
    maybe_get_index,
    maybe_reset_index,
    maybe_set_index,
    validate_strict_and_pass_though,
)
from narwhals.dataframe import DataFrame as NwDataFrame, LazyFrame as NwLazyFrame
from narwhals.exceptions import InvalidIntoExprError
from narwhals.expr import Expr as NwExpr
from narwhals.functions import _new_series_impl, concat, show_versions
from narwhals.schema import Schema as NwSchema
from narwhals.series import Series as NwSeries
from narwhals.stable.v1 import dependencies, dtypes, selectors
from narwhals.stable.v1.dtypes import (
    Array,
    Binary,
    Boolean,
    Categorical,
    Date,
    Datetime,
    Decimal,
    Duration,
    Enum,
    Field,
    Float32,
    Float64,
    Int8,
    Int16,
    Int32,
    Int64,
    Int128,
    List,
    Object,
    String,
    Struct,
    Time,
    UInt8,
    UInt16,
    UInt32,
    UInt64,
    UInt128,
    Unknown,
)
from narwhals.stable.v1.typing import (
    DataFrameT,
    IntoDataFrameT,
    IntoFrame,
    IntoLazyFrameT,
    IntoSeries,
    IntoSeriesT,
    LazyFrameT,
    SeriesT,
)
from narwhals.translate import _from_native_impl, get_native_namespace, to_py_scalar

if TYPE_CHECKING:
    from collections.abc import Iterable, Mapping, Sequence
    from types import ModuleType

    from typing_extensions import ParamSpec, Self, Unpack

    from narwhals._expression_parsing import ExprMetadata
    from narwhals._translate import (
        AllowAnyStrictV1 as AllowAnyStrict,
        AllowAnyV1 as AllowAny,
        AllowLazyStrictV1 as AllowLazyStrict,
        AllowLazyV1 as AllowLazy,
        AllowSeriesStrictV1 as AllowSeriesStrict,
        AllowSeriesV1 as AllowSeries,
        ExcludeSeriesStrictV1 as ExcludeSeriesStrict,
        ExcludeSeriesV1 as ExcludeSeries,
        IntoArrowTable,
        OnlyEagerOrInterchange,
        OnlyEagerOrInterchangeStrict,
        OnlySeriesStrictV1 as OnlySeriesStrict,
        OnlySeriesV1 as OnlySeries,
        PassThroughUnknownV1 as PassThroughUnknown,
        StrictUnknownV1 as StrictUnknown,
    )
    from narwhals._typing import (
        Arrow,
        Backend,
        EagerAllowed,
        IntoBackend,
        LazyAllowed,
        Pandas,
        Polars,
    )
    from narwhals.dataframe import MultiColSelector, MultiIndexSelector
    from narwhals.dtypes import DType
    from narwhals.typing import (
        FileSource,
        IntoDType,
        IntoExpr,
        IntoSchema,
        NonNestedLiteral,
        SingleColSelector,
        SingleIndexSelector,
        _1DArray,
        _2DArray,
    )

    T = TypeVar("T", default=Any)
    P = ParamSpec("P")
    R = TypeVar("R")


# NOTE legit
class DataFrame(NwDataFrame[IntoDataFrameT]):  # type: ignore[type-var]
    _version = Version.V1

    @inherit_doc(NwDataFrame)
    def __init__(self, df: Any, *, level: Literal["full", "lazy", "interchange"]) -> None:
        assert df._version is Version.V1  # noqa: S101
        super().__init__(df, level=level)

    # We need to override any method which don't return Self so that type
    # annotations are correct.

    @classmethod
    def from_arrow(
        cls, native_frame: IntoArrowTable, *, backend: IntoBackend[EagerAllowed]
    ) -> DataFrame[Any]:
        result = super().from_arrow(native_frame, backend=backend)
        return cast("DataFrame[Any]", result)

    @classmethod
    def from_dict(
        cls,
        data: Mapping[str, Any],
        schema: IntoSchema | Mapping[str, DType | None] | None = None,
        *,
        backend: IntoBackend[EagerAllowed] | None = None,
    ) -> DataFrame[Any]:
        result = super().from_dict(data, schema, backend=backend)
        return cast("DataFrame[Any]", result)

    @classmethod
    def from_dicts(
        cls,
        data: Sequence[Any],
        schema: IntoSchema | Mapping[str, DType | None] | None = None,
        *,
        backend: IntoBackend[EagerAllowed],
    ) -> DataFrame[Any]:
        result = super().from_dicts(data, schema, backend=backend)
        return cast("DataFrame[Any]", result)

    @classmethod
    def from_numpy(
        cls,
        data: _2DArray,
        schema: Mapping[str, DType] | Schema | Sequence[str] | None = None,
        *,
        backend: IntoBackend[EagerAllowed],
    ) -> DataFrame[Any]:
        result = super().from_numpy(data, schema, backend=backend)
        return cast("DataFrame[Any]", result)

    @property
    def _series(self) -> type[Series[Any]]:
        return cast("type[Series[Any]]", Series)

    @property
    def _lazyframe(self) -> type[LazyFrame[Any]]:
        return cast("type[LazyFrame[Any]]", LazyFrame)

    @overload
    def __getitem__(self, item: tuple[SingleIndexSelector, SingleColSelector]) -> Any: ...

    @overload
    def __getitem__(  # type: ignore[overload-overlap]
        self, item: str | tuple[MultiIndexSelector, SingleColSelector]
    ) -> Series[Any]: ...

    @overload
    def __getitem__(
        self,
        item: (
            SingleIndexSelector
            | MultiIndexSelector
            | MultiColSelector
            | tuple[SingleIndexSelector, MultiColSelector]
            | tuple[MultiIndexSelector, MultiColSelector]
        ),
    ) -> Self: ...
    def __getitem__(
        self,
        item: (
            SingleIndexSelector
            | SingleColSelector
            | MultiColSelector
            | MultiIndexSelector
            | tuple[SingleIndexSelector, SingleColSelector]
            | tuple[SingleIndexSelector, MultiColSelector]
            | tuple[MultiIndexSelector, SingleColSelector]
            | tuple[MultiIndexSelector, MultiColSelector]
        ),
    ) -> Series[Any] | Self | Any:
        return super().__getitem__(item)

    def get_column(self, name: str) -> Series:
        # Type checkers complain that `nw.Series` is not assignable to `nw.v1.stable.Series`.
        # However the return type actually is `nw.v1.stable.Series`, check `tests/v1_test.py`.
        return super().get_column(name)  # type: ignore[return-value]

    def lazy(
        self,
        backend: IntoBackend[LazyAllowed] | None = None,
        *,
        session: Any | None = None,
    ) -> LazyFrame[Any]:
        return _stableify(super().lazy(backend=backend, session=session))

    @overload  # type: ignore[override]
    def to_dict(self, *, as_series: Literal[True] = ...) -> dict[str, Series[Any]]: ...
    @overload
    def to_dict(self, *, as_series: Literal[False]) -> dict[str, list[Any]]: ...
    @overload
    def to_dict(
        self, *, as_series: bool = True
    ) -> dict[str, Series[Any]] | dict[str, list[Any]]: ...
    def to_dict(  # pyright: ignore[reportIncompatibleMethodOverride]
        self, *, as_series: bool = True
    ) -> dict[str, Series[Any]] | dict[str, list[Any]]:
        # Type checkers complain that `nw.Series` is not assignable to `nw.v1.stable.Series`.
        # However the return type actually is `nw.v1.stable.Series`, check `tests/v1_test.py::test_to_dict_as_series`.
        return super().to_dict(as_series=as_series)  # type: ignore[return-value]

    def is_duplicated(self) -> Series[Any]:
        return _stableify(super().is_duplicated())

    def is_unique(self) -> Series[Any]:
        return _stableify(super().is_unique())

    def _l1_norm(self) -> Self:
        # Private, just used to test the stable API.
        return self.select(all()._l1_norm())


class LazyFrame(NwLazyFrame[IntoLazyFrameT]):
    @inherit_doc(NwLazyFrame)
    def __init__(self, df: Any, *, level: Literal["full", "lazy", "interchange"]) -> None:
        assert df._version is Version.V1  # noqa: S101
        super().__init__(df, level=level)

    @property
    def _dataframe(self) -> type[DataFrame[Any]]:
        return DataFrame

    def _validate_metadata(self, metadata: ExprMetadata) -> None:
        # After v1, we raise for order-dependent operations.
        pass

    def collect(
        self, backend: IntoBackend[Polars | Pandas | Arrow] | None = None, **kwargs: Any
    ) -> DataFrame[Any]:
        return _stableify(super().collect(backend=backend, **kwargs))

    def _l1_norm(self) -> Self:
        # Private, just used to test the stable API.
        return self.select(all()._l1_norm())

    def tail(self, n: int = 5) -> Self:
        r"""Get the last `n` rows."""
        return super().tail(n)

    def gather_every(self, n: int, offset: int = 0) -> Self:
        r"""Take every nth row in the DataFrame and return as a new DataFrame.

        Arguments:
            n: Gather every *n*-th row.
            offset: Starting index.
        """
        return self._with_compliant(
            self._compliant_frame.gather_every(n=n, offset=offset)  # type: ignore[attr-defined]
        )

    def with_row_index(
        self, name: str = "index", *, order_by: str | Sequence[str] | None = None
    ) -> Self:
        order_by_ = [order_by] if isinstance(order_by, str) else order_by
        return self._with_compliant(
            self._compliant_frame.with_row_index(
                name=name,
                order_by=order_by_,  # type: ignore[arg-type]
            )
        )


class Series(NwSeries[IntoSeriesT]):
    _version = Version.V1

    @inherit_doc(NwSeries)
    def __init__(
        self, series: Any, *, level: Literal["full", "lazy", "interchange"]
    ) -> None:
        assert series._version is Version.V1  # noqa: S101
        super().__init__(series, level=level)

    # We need to override any method which don't return Self so that type
    # annotations are correct.

    @classmethod
    def from_numpy(
        cls,
        name: str,
        values: _1DArray,
        dtype: IntoDType | None = None,
        *,
        backend: IntoBackend[EagerAllowed],
    ) -> Series[Any]:
        result = super().from_numpy(name, values, dtype, backend=backend)
        return cast("Series[Any]", result)

    @classmethod
    def from_iterable(
        cls,
        name: str,
        values: Iterable[Any],
        dtype: IntoDType | None = None,
        *,
        backend: IntoBackend[EagerAllowed],
    ) -> Series[Any]:
        result = super().from_iterable(name, values, dtype, backend=backend)
        return cast("Series[Any]", result)

    @property
    def _dataframe(self) -> type[DataFrame[Any]]:
        return DataFrame

    def to_frame(self) -> DataFrame[Any]:
        return _stableify(super().to_frame())

    def value_counts(
        self,
        *,
        sort: bool = False,
        parallel: bool = False,
        name: str | None = None,
        normalize: bool = False,
    ) -> DataFrame[Any]:
        return _stableify(
            super().value_counts(
                sort=sort, parallel=parallel, name=name, normalize=normalize
            )
        )

    def hist(
        self,
        bins: list[float] | None = None,
        *,
        bin_count: int | None = None,
        include_breakpoint: bool = True,
    ) -> DataFrame[Any]:
        from narwhals.exceptions import NarwhalsUnstableWarning

        msg = (
            "`Series.hist` is being called from the stable API although considered "
            "an unstable feature."
        )
        issue_warning(msg, NarwhalsUnstableWarning)
        return _stableify(
            super().hist(
                bins=bins, bin_count=bin_count, include_breakpoint=include_breakpoint
            )
        )


class Expr(NwExpr):
    def _l1_norm(self) -> Self:
        return super()._taxicab_norm()

    def head(self, n: int = 10) -> Self:
        r"""Get the first `n` rows."""
        return self._append_node(ExprNode(ExprKind.ORDERABLE_FILTRATION, "head", n=n))

    def tail(self, n: int = 10) -> Self:
        r"""Get the last `n` rows."""
        return self._append_node(ExprNode(ExprKind.ORDERABLE_FILTRATION, "tail", n=n))

    def gather_every(self, n: int, offset: int = 0) -> Self:
        r"""Take every nth value in the Series and return as new Series.

        Arguments:
            n: Gather every *n*-th row.
            offset: Starting index.
        """
        return self._append_node(
            ExprNode(ExprKind.ORDERABLE_FILTRATION, "gather_every", n=n, offset=offset)
        )

    def unique(self, *, maintain_order: bool | None = None) -> Self:
        """Return unique values of this expression."""
        if maintain_order is not None:
            msg = (
                "`maintain_order` has no effect and is only kept around for backwards-compatibility. "
                "You can safely remove this argument."
            )
            issue_warning(msg, UserWarning)
        return self._append_node(ExprNode(ExprKind.FILTRATION, "unique"))

    def sort(self, *, descending: bool = False, nulls_last: bool = False) -> Self:
        """Sort this column. Place null values first."""
        return self._append_node(
            ExprNode(
                ExprKind.WINDOW, "sort", descending=descending, nulls_last=nulls_last
            )
        )

    def arg_max(self) -> Self:
        """Returns the index of the maximum value."""
        return self._append_node(ExprNode(ExprKind.ORDERABLE_AGGREGATION, "arg_max"))

    def arg_min(self) -> Self:
        """Returns the index of the minimum value."""
        return self._append_node(ExprNode(ExprKind.ORDERABLE_AGGREGATION, "arg_min"))

    def arg_true(self) -> Self:
        """Find elements where boolean expression is True."""
        return self._append_node(ExprNode(ExprKind.ORDERABLE_FILTRATION, "arg_true"))

    def sample(
        self,
        n: int | None = None,
        *,
        fraction: float | None = None,
        with_replacement: bool = False,
        seed: int | None = None,
    ) -> Self:
        """Sample randomly from this expression.

        Arguments:
            n: Number of items to return. Cannot be used with fraction.
            fraction: Fraction of items to return. Cannot be used with n.
            with_replacement: Allow values to be sampled more than once.
            seed: Seed for the random number generator. If set to None (default), a random
                seed is generated for each sample operation.
        """
        return self._append_node(
            ExprNode(
                ExprKind.FILTRATION,
                "sample",
                n=n,
                fraction=fraction,
                with_replacement=with_replacement,
                seed=seed,
            )
        )


class Schema(NwSchema):
    _version = Version.V1

    @inherit_doc(NwSchema)
    def __init__(
        self, schema: Mapping[str, DType] | Iterable[tuple[str, DType]] | None = None
    ) -> None:
        super().__init__(schema)


@overload
def _stableify(obj: NwDataFrame[IntoDataFrameT]) -> DataFrame[IntoDataFrameT]: ...  # type: ignore[type-var]
@overload
def _stableify(obj: NwLazyFrame[IntoLazyFrameT]) -> LazyFrame[IntoLazyFrameT]: ...
@overload
def _stableify(obj: NwSeries[IntoSeriesT]) -> Series[IntoSeriesT]: ...
@overload
def _stableify(obj: NwExpr) -> Expr: ...


def _stableify(
    obj: NwDataFrame[IntoDataFrameT]  # type: ignore[type-var]
    | NwLazyFrame[IntoLazyFrameT]
    | NwSeries[IntoSeriesT]
    | NwExpr,
) -> DataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT] | Expr:
    if isinstance(obj, NwDataFrame):
        return DataFrame(obj._compliant_frame._with_version(Version.V1), level=obj._level)
    if isinstance(obj, NwLazyFrame):
        return LazyFrame(obj._compliant_frame._with_version(Version.V1), level=obj._level)
    if isinstance(obj, NwSeries):
        return Series(obj._compliant_series._with_version(Version.V1), level=obj._level)
    if isinstance(obj, NwExpr):
        return Expr(*obj._nodes)
    assert_never(obj)


@overload
def from_native(native_object: SeriesT, **kwds: Unpack[OnlySeries]) -> SeriesT: ...
@overload
def from_native(native_object: SeriesT, **kwds: Unpack[OnlySeriesStrict]) -> SeriesT: ...
@overload
def from_native(native_object: SeriesT, **kwds: Unpack[AllowSeries]) -> SeriesT: ...
@overload
def from_native(native_object: SeriesT, **kwds: Unpack[AllowSeriesStrict]) -> SeriesT: ...
@overload
def from_native(
    native_object: DataFrameT, **kwds: Unpack[ExcludeSeries]
) -> DataFrameT: ...
@overload
def from_native(
    native_object: DataFrameT, **kwds: Unpack[ExcludeSeriesStrict]
) -> DataFrameT: ...
@overload
def from_native(native_object: LazyFrameT, **kwds: Unpack[AllowLazy]) -> LazyFrameT: ...
@overload
def from_native(
    native_object: LazyFrameT, **kwds: Unpack[AllowLazyStrict]
) -> LazyFrameT: ...
@overload
def from_native(
    native_object: IntoDataFrameT, **kwds: Unpack[OnlyEagerOrInterchange]
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
    native_object: IntoDataFrameT, **kwds: Unpack[OnlyEagerOrInterchangeStrict]
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
    native_object: IntoDataFrameT, **kwds: Unpack[ExcludeSeries]
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
    native_object: IntoDataFrameT, **kwds: Unpack[ExcludeSeriesStrict]
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
    native_object: IntoSeriesT, **kwds: Unpack[OnlySeries]
) -> Series[IntoSeriesT]: ...
@overload
def from_native(
    native_object: IntoSeriesT, **kwds: Unpack[OnlySeriesStrict]
) -> Series[IntoSeriesT]: ...
@overload
def from_native(
    native_object: IntoDataFrameT | IntoSeriesT, **kwds: Unpack[AllowSeries]
) -> DataFrame[IntoDataFrameT] | Series[IntoSeriesT]: ...
@overload
def from_native(
    native_object: IntoDataFrameT | IntoSeriesT, **kwds: Unpack[AllowSeriesStrict]
) -> DataFrame[IntoDataFrameT] | Series[IntoSeriesT]: ...
@overload
def from_native(
    native_object: IntoLazyFrameT, **kwds: Unpack[AllowLazy]
) -> LazyFrame[IntoLazyFrameT]: ...
@overload
def from_native(
    native_object: IntoLazyFrameT, **kwds: Unpack[AllowLazyStrict]
) -> LazyFrame[IntoLazyFrameT]: ...
@overload
def from_native(
    native_object: IntoDataFrameT | IntoLazyFrameT | IntoSeriesT, **kwds: Unpack[AllowAny]
) -> DataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT]: ...
@overload
def from_native(
    native_object: IntoDataFrameT | IntoLazyFrameT | IntoSeriesT,
    **kwds: Unpack[AllowAnyStrict],
) -> DataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT]: ...
@overload
def from_native(native_object: T, **kwds: Unpack[PassThroughUnknown]) -> T: ...
@overload
def from_native(native_object: T, **kwds: Unpack[StrictUnknown]) -> T: ...


# All params passed in as variables
@overload
def from_native(
    native_object: Any,
    *,
    pass_through: bool,
    eager_only: bool,
    eager_or_interchange_only: bool = False,
    series_only: bool,
    allow_series: bool | None,
) -> Any: ...


def from_native(
    native_object: IntoDataFrameT
    | IntoLazyFrameT
    | IntoFrame
    | IntoSeriesT
    | IntoSeries
    | T,
    *,
    strict: bool | None = None,
    pass_through: bool | None = None,
    eager_only: bool = False,
    eager_or_interchange_only: bool = False,
    series_only: bool = False,
    allow_series: bool | None = None,
    **kwds: Any,
) -> LazyFrame[IntoLazyFrameT] | DataFrame[IntoDataFrameT] | Series[IntoSeriesT] | T:
    """Convert `native_object` to Narwhals Dataframe, Lazyframe, or Series.

    See `narwhals.from_native` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    # Early returns
    if isinstance(native_object, (DataFrame, LazyFrame)) and not series_only:
        return native_object
    if isinstance(native_object, Series) and (series_only or allow_series):
        return native_object

    pass_through = validate_strict_and_pass_though(
        strict, pass_through, pass_through_default=False
    )
    if kwds:
        msg = f"from_native() got an unexpected keyword argument {next(iter(kwds))!r}"
        raise TypeError(msg)

    return _from_native_impl(  # type: ignore[no-any-return]
        native_object,
        pass_through=pass_through,
        eager_only=eager_only,
        eager_or_interchange_only=eager_or_interchange_only,
        series_only=series_only,
        allow_series=allow_series,
        version=Version.V1,
    )


@overload
def to_native(
    narwhals_object: DataFrame[IntoDataFrameT], *, strict: Literal[True] = ...
) -> IntoDataFrameT: ...
@overload
def to_native(
    narwhals_object: LazyFrame[IntoLazyFrameT], *, strict: Literal[True] = ...
) -> IntoLazyFrameT: ...
@overload
def to_native(
    narwhals_object: Series[IntoSeriesT], *, strict: Literal[True] = ...
) -> IntoSeriesT: ...
@overload
def to_native(narwhals_object: Any, *, strict: bool) -> Any: ...
@overload
def to_native(
    narwhals_object: DataFrame[IntoDataFrameT], *, pass_through: Literal[False] = ...
) -> IntoDataFrameT: ...
@overload
def to_native(
    narwhals_object: LazyFrame[IntoLazyFrameT], *, pass_through: Literal[False] = ...
) -> IntoLazyFrameT: ...
@overload
def to_native(
    narwhals_object: Series[IntoSeriesT], *, pass_through: Literal[False] = ...
) -> IntoSeriesT: ...
@overload
def to_native(narwhals_object: Any, *, pass_through: bool) -> Any: ...


def to_native(
    narwhals_object: DataFrame[IntoDataFrameT]
    | LazyFrame[IntoLazyFrameT]
    | Series[IntoSeriesT],
    *,
    strict: bool | None = None,
    pass_through: bool | None = None,
) -> IntoLazyFrameT | IntoDataFrameT | IntoSeriesT | Any:
    """Convert Narwhals object to native one.

    See `narwhals.to_native` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    from narwhals._utils import validate_strict_and_pass_though

    pass_through = validate_strict_and_pass_though(
        strict, pass_through, pass_through_default=False
    )
    return nw.to_native(narwhals_object, pass_through=pass_through)


def narwhalify(
    func: Callable[..., Any] | None = None,
    *,
    strict: bool | None = None,
    pass_through: bool | None = None,
    eager_only: bool = False,
    eager_or_interchange_only: bool = False,
    series_only: bool = False,
    allow_series: bool | None = True,
) -> Callable[..., Any]:
    """Decorate function so it becomes dataframe-agnostic.

    See `narwhals.narwhalify` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    pass_through = validate_strict_and_pass_though(
        strict, pass_through, pass_through_default=True
    )

    def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
        @wraps(func)
        def wrapper(*args: Any, **kwargs: Any) -> Any:
            args = [
                from_native(
                    arg,
                    pass_through=pass_through,
                    eager_only=eager_only,
                    eager_or_interchange_only=eager_or_interchange_only,
                    series_only=series_only,
                    allow_series=allow_series,
                )
                for arg in args
            ]  # type: ignore[assignment]

            kwargs = {
                name: from_native(
                    value,
                    pass_through=pass_through,
                    eager_only=eager_only,
                    eager_or_interchange_only=eager_or_interchange_only,
                    series_only=series_only,
                    allow_series=allow_series,
                )
                for name, value in kwargs.items()
            }

            backends = {
                b()
                for v in (*args, *kwargs.values())
                if (b := getattr(v, "__native_namespace__", None))
            }

            if backends.__len__() > 1:
                msg = "Found multiple backends. Make sure that all dataframe/series inputs come from the same backend."
                raise ValueError(msg)

            result = func(*args, **kwargs)

            return to_native(result, pass_through=pass_through)

        return wrapper

    if func is None:
        return decorator
    # If func is not None, it means the decorator is used without arguments
    return decorator(func)


def all() -> Expr:
    return _stableify(nw.all())


def col(*names: str | Iterable[str]) -> Expr:
    return _stableify(nw.col(*names))


def exclude(*names: str | Iterable[str]) -> Expr:
    return _stableify(nw.exclude(*names))


def nth(*indices: int | Sequence[int]) -> Expr:
    return _stableify(nw.nth(*indices))


def len() -> Expr:
    return _stableify(nw.len())


def lit(value: NonNestedLiteral, dtype: IntoDType | None = None) -> Expr:
    return _stableify(nw.lit(value, dtype))


def min(*columns: str) -> Expr:
    return _stableify(nw.min(*columns))


def max(*columns: str) -> Expr:
    return _stableify(nw.max(*columns))


def mean(*columns: str) -> Expr:
    return _stableify(nw.mean(*columns))


def median(*columns: str) -> Expr:
    return _stableify(nw.median(*columns))


def sum(*columns: str) -> Expr:
    return _stableify(nw.sum(*columns))


def sum_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
    return _stableify(nw.sum_horizontal(*exprs))


def all_horizontal(
    *exprs: IntoExpr | Iterable[IntoExpr], ignore_nulls: bool = False
) -> Expr:
    return _stableify(nw.all_horizontal(*exprs, ignore_nulls=ignore_nulls))


def any_horizontal(
    *exprs: IntoExpr | Iterable[IntoExpr], ignore_nulls: bool = False
) -> Expr:
    return _stableify(nw.any_horizontal(*exprs, ignore_nulls=ignore_nulls))


def mean_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
    return _stableify(nw.mean_horizontal(*exprs))


def min_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
    return _stableify(nw.min_horizontal(*exprs))


def max_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
    return _stableify(nw.max_horizontal(*exprs))


def concat_str(
    exprs: IntoExpr | Iterable[IntoExpr],
    *more_exprs: IntoExpr,
    separator: str = "",
    ignore_nulls: bool = False,
) -> Expr:
    return _stableify(
        nw.concat_str(exprs, *more_exprs, separator=separator, ignore_nulls=ignore_nulls)
    )


def format(f_string: str, *args: IntoExpr) -> Expr:
    """Format expressions as a string."""
    return _stableify(nw.format(f_string, *args))


def coalesce(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) -> Expr:
    return _stableify(nw.coalesce(exprs, *more_exprs))


def get_level(
    obj: DataFrame[Any] | LazyFrame[Any] | Series[IntoSeriesT],
) -> Literal["full", "lazy", "interchange"]:
    """Level of support Narwhals has for current object.

    Arguments:
        obj: Dataframe or Series.

    Returns:
        This can be one of

            - 'full': full Narwhals API support
            - 'lazy': only lazy operations are supported. This excludes anything
              which involves iterating over rows in Python.
            - 'interchange': only metadata operations are supported (`df.schema`)
    """
    return obj._level


class When(nw_f.When):
    @classmethod
    def from_when(cls, when: nw_f.When) -> When:
        return cls(when._predicate)

    def then(self, value: IntoExpr | NonNestedLiteral | _1DArray) -> Then:
        return Then.from_then(super().then(value))


class Then(nw_f.Then, Expr):
    @classmethod
    def from_then(cls, then: nw_f.Then) -> Then:
        return cls(*then._nodes)

    def otherwise(self, value: IntoExpr | NonNestedLiteral | _1DArray) -> Expr:
        return _stableify(super().otherwise(value))


def when(*predicates: IntoExpr | Iterable[IntoExpr]) -> When:
    return When.from_when(nw_f.when(*predicates))


@deprecate_native_namespace(required=True)
def new_series(
    name: str,
    values: Any,
    dtype: IntoDType | None = None,
    *,
    backend: IntoBackend[EagerAllowed] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
) -> Series[Any]:
    """Instantiate Narwhals Series from iterable (e.g. list or array).

    See `narwhals.new_series` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    backend = cast("IntoBackend[EagerAllowed]", backend)
    return _stableify(_new_series_impl(name, values, dtype, backend=backend))


@deprecate_native_namespace(required=True)
def from_arrow(
    native_frame: IntoArrowTable,
    *,
    backend: IntoBackend[EagerAllowed] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
) -> DataFrame[Any]:
    """Construct a DataFrame from an object which supports the PyCapsule Interface.

    See `narwhals.from_arrow` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    backend = cast("IntoBackend[EagerAllowed]", backend)
    return _stableify(nw_f.from_arrow(native_frame, backend=backend))


@deprecate_native_namespace()
def from_dict(
    data: Mapping[str, Any],
    schema: Mapping[str, DType] | Schema | None = None,
    *,
    backend: IntoBackend[EagerAllowed] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
) -> DataFrame[Any]:
    """Instantiate DataFrame from dictionary.

    See `narwhals.from_dict` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    return _stableify(nw_f.from_dict(data, schema, backend=backend))


from_dicts: Final = DataFrame.from_dicts


@deprecate_native_namespace(required=True)
def from_numpy(
    data: _2DArray,
    schema: Mapping[str, DType] | Schema | Sequence[str] | None = None,
    *,
    backend: IntoBackend[EagerAllowed] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
) -> DataFrame[Any]:
    """Construct a DataFrame from a NumPy ndarray.

    See `narwhals.from_numpy` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    backend = cast("IntoBackend[EagerAllowed]", backend)
    return _stableify(nw_f.from_numpy(data, schema, backend=backend))


@deprecate_native_namespace(required=True)
def read_csv(
    source: FileSource,
    *,
    backend: IntoBackend[EagerAllowed] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
    **kwargs: Any,
) -> DataFrame[Any]:
    """Read a CSV file into a DataFrame.

    See `narwhals.read_csv` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    backend = cast("IntoBackend[EagerAllowed]", backend)
    return _stableify(nw_f.read_csv(source, backend=backend, **kwargs))


@deprecate_native_namespace(required=True)
def scan_csv(
    source: FileSource,
    *,
    backend: IntoBackend[Backend] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
    **kwargs: Any,
) -> LazyFrame[Any]:
    """Lazily read from a CSV file.

    See `narwhals.scan_csv` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    backend = cast("IntoBackend[Backend]", backend)
    return _stableify(nw_f.scan_csv(source, backend=backend, **kwargs))


@deprecate_native_namespace(required=True)
def read_parquet(
    source: FileSource,
    *,
    backend: IntoBackend[EagerAllowed] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
    **kwargs: Any,
) -> DataFrame[Any]:
    """Read into a DataFrame from a parquet file.

    See `narwhals.read_parquet` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    backend = cast("IntoBackend[EagerAllowed]", backend)
    return _stableify(nw_f.read_parquet(source, backend=backend, **kwargs))


@deprecate_native_namespace(required=True)
def scan_parquet(
    source: FileSource,
    *,
    backend: IntoBackend[Backend] | None = None,
    native_namespace: ModuleType | None = None,  # noqa: ARG001
    **kwargs: Any,
) -> LazyFrame[Any]:
    """Lazily read from a parquet file.

    See `narwhals.scan_parquet` for full docstring. Note that `native_namespace` is
    an is the same as `backend` but only accepts module types - for new code, we
    recommend using `backend`, as that's available beyond just `narwhals.stable.v1`.
    """
    backend = cast("IntoBackend[Backend]", backend)
    return _stableify(nw_f.scan_parquet(source, backend=backend, **kwargs))


__all__ = [
    "Array",
    "Binary",
    "Boolean",
    "Categorical",
    "DataFrame",
    "Date",
    "Datetime",
    "Decimal",
    "Duration",
    "Enum",
    "Expr",
    "Field",
    "Float32",
    "Float64",
    "Implementation",
    "Int8",
    "Int16",
    "Int32",
    "Int64",
    "Int128",
    "InvalidIntoExprError",
    "LazyFrame",
    "List",
    "Object",
    "Schema",
    "Series",
    "String",
    "Struct",
    "Time",
    "UInt8",
    "UInt16",
    "UInt32",
    "UInt64",
    "UInt128",
    "Unknown",
    "all",
    "all_horizontal",
    "any_horizontal",
    "coalesce",
    "col",
    "concat",
    "concat_str",
    "dependencies",
    "dtypes",
    "exceptions",
    "exclude",
    "format",
    "from_arrow",
    "from_dict",
    "from_dicts",
    "from_native",
    "from_numpy",
    "generate_temporary_column_name",
    "get_level",
    "get_native_namespace",
    "is_expr",
    "is_ordered_categorical",
    "len",
    "lit",
    "max",
    "max_horizontal",
    "maybe_align_index",
    "maybe_convert_dtypes",
    "maybe_get_index",
    "maybe_reset_index",
    "maybe_set_index",
    "mean",
    "mean_horizontal",
    "median",
    "min",
    "min_horizontal",
    "narwhalify",
    "new_series",
    "nth",
    "read_csv",
    "read_parquet",
    "scan_csv",
    "scan_parquet",
    "selectors",
    "show_versions",
    "sum",
    "sum_horizontal",
    "to_native",
    "to_py_scalar",
    "when",
]
